Non-intrusive human activity monitoring in a smart home environment

By S. Mohsen Amiri, Mahsa T. Pourazad, Panos Nasiopoulos, Victor C. M. Leung. 2013

21 citations recorded by the source corpus.

Non-intrusive activity monitoring of occupants in a home environment plays an important role in developing the next generation of smart environments and remote health monitoring systems. One important challenge in this research area is lack of a comprehensive dataset. In this paper, we introduce a new dataset related to a smart home environment, which can be used for human activity recognition. In addition, we benchmark our proposed human action recognition algorithm and some other state-of-the-art methods using our dataset.

Citations in the corpus

• RGB-D-based action recognition datasets: A survey (2016, neutral): “DMLSmartActions DMLSmartActions dataset [43] (http://dml.”
• PKU-MMD: A Large Scale Benchmark for Continuous Multi-Modal Human Action Understanding (2017, neutral): “MSR-Action3D [17] 20 567 567 1 D+Skeleton No 2010 RGBD-HuDaAct [21] 13 1189 1189 1 RGB+D No 2011 MSR-DailyActivity [43] 16 320 320 1 RGB+D+Skeleton No 2012 Act4 [8] 14 6844 6844 1 RGB+D No 2012 UTKinect-Action [53] 10 200 200 1 RGB+D+Skeleton No 2012 3D Action Pairs [23] 12 360 360 1 RGB+D+Skeleton ”
• A similarity measure for analyzing human activities using human-object interaction context (2014, methodology): “Achieved accuracies of different algorithms on the DMLSmarthome dataset [26]”
• RGBD Datasets: Past, Present and Future (2016, unknown): “DML-SmartAction [5] 16 12 932 Continuous recording.”